{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UNITID</th>\n",
       "      <th>OPEID</th>\n",
       "      <th>opeid6</th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>INSTURL</th>\n",
       "      <th>NPCURL</th>\n",
       "      <th>HCM2</th>\n",
       "      <th>PREDDEG</th>\n",
       "      <th>...</th>\n",
       "      <th>RET_PTL4</th>\n",
       "      <th>PCTFLOAN</th>\n",
       "      <th>UG25abv</th>\n",
       "      <th>GRAD_DEBT_MDN_SUPP</th>\n",
       "      <th>GRAD_DEBT_MDN10YR_SUPP</th>\n",
       "      <th>RPY_3YR_RT_SUPP</th>\n",
       "      <th>C150_4_POOLED_SUPP</th>\n",
       "      <th>C200_L4_POOLED_SUPP</th>\n",
       "      <th>md_earn_wne_p10</th>\n",
       "      <th>gt_25k_p6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100654</td>\n",
       "      <td>100200</td>\n",
       "      <td>1002</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AL</td>\n",
       "      <td>www.aamu.edu/</td>\n",
       "      <td>galileo.aamu.edu/netpricecalculator/npcalc.htm</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8204</td>\n",
       "      <td>0.1049</td>\n",
       "      <td>33611.5</td>\n",
       "      <td>373.1566</td>\n",
       "      <td>0.4447139</td>\n",
       "      <td>0.3087183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31400</td>\n",
       "      <td>0.462298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100663</td>\n",
       "      <td>105200</td>\n",
       "      <td>1052</td>\n",
       "      <td>University of Alabama at Birmingham</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>www.uab.edu</td>\n",
       "      <td>www.collegeportraits.org/AL/UAB/estimator/agree</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5397</td>\n",
       "      <td>0.2422</td>\n",
       "      <td>23117</td>\n",
       "      <td>256.6461</td>\n",
       "      <td>0.7562667</td>\n",
       "      <td>0.5085498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40300</td>\n",
       "      <td>0.6604845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100690</td>\n",
       "      <td>2503400</td>\n",
       "      <td>25034</td>\n",
       "      <td>Amridge University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>www.amridgeuniversity.edu</td>\n",
       "      <td>tcc.noellevitz.com/(S(miwoihs5stz5cpyifh4nczu0...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7629</td>\n",
       "      <td>0.8540</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "      <td>0.6472492</td>\n",
       "      <td>PrivacySuppressed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38100</td>\n",
       "      <td>0.6466666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100706</td>\n",
       "      <td>105500</td>\n",
       "      <td>1055</td>\n",
       "      <td>University of Alabama in Huntsville</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>AL</td>\n",
       "      <td>www.uah.edu</td>\n",
       "      <td>finaid.uah.edu/</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4728</td>\n",
       "      <td>0.2640</td>\n",
       "      <td>24738</td>\n",
       "      <td>274.6425</td>\n",
       "      <td>0.7819979</td>\n",
       "      <td>0.4782113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46600</td>\n",
       "      <td>0.6605657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100724</td>\n",
       "      <td>100500</td>\n",
       "      <td>1005</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>www.alasu.edu/email/index.aspx</td>\n",
       "      <td>www.alasu.edu/cost-aid/forms/calculator/index....</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8735</td>\n",
       "      <td>0.1270</td>\n",
       "      <td>33452</td>\n",
       "      <td>371.3858</td>\n",
       "      <td>0.3311989</td>\n",
       "      <td>0.257482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27800</td>\n",
       "      <td>0.3422256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNITID    OPEID  opeid6                               INSTNM        CITY  \\\n",
       "0  100654   100200    1002             Alabama A & M University      Normal   \n",
       "1  100663   105200    1052  University of Alabama at Birmingham  Birmingham   \n",
       "2  100690  2503400   25034                   Amridge University  Montgomery   \n",
       "3  100706   105500    1055  University of Alabama in Huntsville  Huntsville   \n",
       "4  100724   100500    1005             Alabama State University  Montgomery   \n",
       "\n",
       "  STABBR                         INSTURL  \\\n",
       "0     AL                   www.aamu.edu/   \n",
       "1     AL                     www.uab.edu   \n",
       "2     AL       www.amridgeuniversity.edu   \n",
       "3     AL                     www.uah.edu   \n",
       "4     AL  www.alasu.edu/email/index.aspx   \n",
       "\n",
       "                                              NPCURL  HCM2  PREDDEG  \\\n",
       "0     galileo.aamu.edu/netpricecalculator/npcalc.htm     0        3   \n",
       "1    www.collegeportraits.org/AL/UAB/estimator/agree     0        3   \n",
       "2  tcc.noellevitz.com/(S(miwoihs5stz5cpyifh4nczu0...     0        3   \n",
       "3                                    finaid.uah.edu/     0        3   \n",
       "4  www.alasu.edu/cost-aid/forms/calculator/index....     0        3   \n",
       "\n",
       "     ...      RET_PTL4  PCTFLOAN  UG25abv  GRAD_DEBT_MDN_SUPP  \\\n",
       "0    ...           NaN    0.8204   0.1049             33611.5   \n",
       "1    ...           NaN    0.5397   0.2422               23117   \n",
       "2    ...           NaN    0.7629   0.8540   PrivacySuppressed   \n",
       "3    ...           NaN    0.4728   0.2640               24738   \n",
       "4    ...           NaN    0.8735   0.1270               33452   \n",
       "\n",
       "   GRAD_DEBT_MDN10YR_SUPP  RPY_3YR_RT_SUPP  C150_4_POOLED_SUPP  \\\n",
       "0                373.1566        0.4447139           0.3087183   \n",
       "1                256.6461        0.7562667           0.5085498   \n",
       "2       PrivacySuppressed        0.6472492   PrivacySuppressed   \n",
       "3                274.6425        0.7819979           0.4782113   \n",
       "4                371.3858        0.3311989            0.257482   \n",
       "\n",
       "   C200_L4_POOLED_SUPP  md_earn_wne_p10  gt_25k_p6  \n",
       "0                  NaN            31400   0.462298  \n",
       "1                  NaN            40300  0.6604845  \n",
       "2                  NaN            38100  0.6466666  \n",
       "3                  NaN            46600  0.6605657  \n",
       "4                  NaN            27800  0.3422256  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(r'D:\\我的资料库\\Links\\cccc\\ProblemCDATA\\Problem C - Most Recent Cohorts Data (Scorecard Elements).xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2372.000000\n",
       "mean        0.477151\n",
       "std         0.202158\n",
       "min         0.022435\n",
       "25%         0.333250\n",
       "50%         0.467876\n",
       "75%         0.612556\n",
       "max         1.000000\n",
       "Name: C150_4_POOLED_SUPP, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C150 = df['C150_4_POOLED_SUPP']\n",
    "C150.replace('PrivacySuppressed', np.nan, inplace=True)\n",
    "C150.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc118d68>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc100be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C150.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7786.000000\n",
       "mean        0.469750\n",
       "std         0.212864\n",
       "min         0.022435\n",
       "25%         0.310600\n",
       "50%         0.455198\n",
       "75%         0.606039\n",
       "max         1.000000\n",
       "Name: C150_4_POOLED_SUPP, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C150_2 = C150.interpolate(method='nearest',order=3)\n",
    "C150_2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf9ae8d0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXfcFcX1/z8DCCgIioIFQbA3MGrUqFEeA/aKimKJDf1a\noxg1RqMG8yOmaLCXJMYao0mwF9RYEFtiRAVELIgNC6BioZdnf3/M3dy5e6fvzO7c+8z79bqve+/u\n7MzZ2dmzZ8+cmSFJkiASiUQijUu7sgWIRCKRSD6iIo9EIpEGJyrySCQSaXCiIo9EIpEGJyrySCQS\naXCiIo9EIpEGR6nICSFXE0JmEUJaCSEPSdIdSAiZTghZSAh5hhDSz6WgkUgkEuGjY5EnAO5iftdB\nCFkTwN0AvgZwDoBtANzmQsBIJBKJyFEq8iRJzgRwhSLZ4QA6AvhNkiTXAbgfwM6EkPXyixiJRCIR\nGbo+cqLY37/y/Unle2ZmeyQSiUQ84auzU6X4I5FIJOKIDrYHEkI6A1ieJMlSADMqm/sA+DeA3pX/\nMzjHxcldIpFIxIIkSbhGsk7Uyj4ADqv87UsIGUEI2RDAAgCvVrbfDWAJgPMIIT8BMBTAc0mSvC8Q\npiE+v/zlL0uXIcraXLKOHJkAaAxZG6le24KsMnRcK+cA+A1oxMpAAH8CsGOqkyuK+XPQDs9VAFwG\nYCKAYzXyjkQikUhOlK6VJEl2Fey6LZPuPgD3uRAqEolEIvrEkZ0SWlpayhZBmyirH6KsfoiyuoWo\nfC/OCyQkKbrMLF99Bay8MrDCCqWKYcyppwIXXgisvXbZkkRsGTkSuOoqoORbINKAEEKQ2HZ2NiOb\nbAJccknZUphzww3A448XV16SAK2txZUXiYiYNAk4/fTYHkW0SUU+Zw7wxhtlSxE+558PDB1athSR\nRuftt4HvvsuXx0svAdddByxd6kamZqNpFPmsWcBbb5UtRXH8+9/ASSf5LWPsWODBB/2W4Yvbbwfe\nfbdsKeohbXCo3CabAL/6VdlSNDdNo8iHDgU23bRsKfzw3nv12267DfjTn9yWM3s2MHOmOl0jcMwx\n4SuP448H7rjDX/4h+eE/+USdJmJP0yjyhQvLlqCeJUvy5zF3LrDBBvnz0eHww4EhQ6r/Q1IEeXnl\nlbIloLB1esst7h/GKY8+CnTq5CfvMkjrrZnapEuaRpGHxuLFdjdSayvw2WfV/8uWuZNJxaRJ1J/p\nmtdec5+nKdtuS91vbYXp06M/uS0RFbknbBXwE0/ohRe68LW+8w71g4vgWT833ggccgj9PXcu7YRS\nsfXWtQ+nsggx4iFamBEXWE+a1ai4vnEWLaLK8Kij3OQ3d658v8vOsnPPpZ2ZJnVy2220oxUALrqI\nRhLoHN8uAJPB1bX/9lv6cJo+3U1+kbZDaytw+eXAggXVbSNGAH365Ms3gNurOC6+2H043bhxwI9/\n7C4/1SAlVRjXkiXABx84E6eOb76p/jaxcH0PviKEdtYWwccf13dAn38+8Ic/mOcVLXI9msVH/tVX\nwC9/Wf1/773As8/mzzdYi/yrr4AePfTT61zgsWOBadPo7wceoJZVt2528vlCpfDOOAPYeWexZX7j\njcCZZ+rVhyoNb/+iRdV9Olb28uX0uwiL/LvvgF69arfNn1+VwWfo329/S9vr2Wf7KyPSHHTpAowa\nRX+7eqsL1iJfbTXghRdop+EWW1RvRpfMn29+zN/+Blx5pXtZUjowj9azz6YdkFm+/BL45z/5x3/6\nqR+5UlhlKFPOy5YBjzxSbGdtliShLq/99qv+lzFoUL4bq9GtxUjjEqwiB6hVvnAhMHWqWiHoWFsu\nbrSf/Qw466z8+YhgLfIxY4CHHqK/2U7FJKGjU3mYnGO2zkzq8OuvgWuuEaebMAHYd9/yldtTT1FZ\ndJgwoT5qZ+lS+vbmi7LrxyfnnQdsvz39nfdtqFnqydd5BK3IQ7x4vmXq2LH2f+qH3nHH+rR5yZ6L\n6j/LlClmeZcBIeZyZNM/8QRw4IF2x7Z1XnoJePllt3k2Yx27OKegFfkBBwAffmh37LffupWlKLKW\nS6gN1zSUr4jzKGr4e6jXJGRinVXx0U6DVuQAcMEFeunYhvLRR0D37n7k0cXVxdK9AT78MP/NoiNz\nOlo1hBtzxRVpf4FL8lw3mzoJoR4j5eFKTwSvyG3CjubN8yOLCa5uUJ7ly8u7Xz/gxRfdlCkrJ50z\nQ9ci96moFi2ivvoUncFJKkzkfeed2v9saKaL/Bsd9lyjj5zSJn3kQP2Jn3yyOGIjxdVTbs4c4Isv\n3ORli4kLI+8DTNbIWltp2KNOWlHenTvXK7+UOXOqA41s4Q3K8qkANt44fx7NoqCKohnqq026VrKK\n7I9/LE6Rb745HcFnQ9GulTStr4a+bBnw/PPV/zbD3RcvphFIPE4/HdhhB/M8r7tOHJrqoi5417Et\nTkUb8UfTd3YCfIVhciOxFrVphc2ZUz9HiKzsN990PwLNdn6Qm24yP8akXl3PW2Kb3xVXAJ9/rpc2\nBGvOJuQzEsa1C5mGVOQmCwb07AlMnOhOHhmbbw7897/FlCUiVQwnnug2X5PQRJt0jYTuOS1bRucc\nd5FXI+Lj3Bq9vrLyt+nOTtW0qNnKSTvEXFhDqoa0eHFt3q2t+S5WmWF+bF6mitxW8bskG0cuuw6u\nB/3MnEnnfrnlltrtRddDoyu+ZiT6yDVIEvHQ+yIatWsFphu14ptsmSFOCQvIp3KQ1dvdd/O32950\nffroLRrh61qm+T71lLs8lyyh9ZEaK5FwCF6Rmzb0P/4R2GYbP3kD+je2K1+5icLkKVtf84CL5Drq\nKGDGDPFxH35YnfvEFex5n3++OJ3OohkuFetXX7nLy5a8ix6zpAtVlLFgRbO8WfDOo010dpqe5Ouv\ni/Pw8Urju7Mqz0W+6Sa9RSp0yta1yO+8E3jsMXFeU6YADz8sLrNLF+A3vzGTk+Wyy+hkXTwGDxYf\n12z+3LRd+phsrkyaRaG7JnhFng5ykV3ACROqswSm06zy4OXx7bf5hvP7blh58hdNrOVCDtednani\nWbAg//wcw4ebl18WruUbPhzYZZfq/zJdYKHXfVmwxl+b6ezUsSguu6z6m+e/k1XWNtvou2JM8Ola\n0V3U2Wa+cR6HHKJnkbNpJk/Wy9sFokFGbTGO/MUXgeeeq/53aZG7aNMTJlRHB9uUHeETvCI3xaQj\nhhA6/3TIS3bxGrBMkfuIWrnnHr1y9t+/uu/ii+nvIuYjz7pMfE/R4LKOfcfjh+ZaGTQon+us0Wmz\nQ/RNKTuawuRC3Xqr+sFjErVi2khMLEsd10rq+2b3pYs7u27AzWKh+R7jEIoiZ9tanod7s1x3ljbR\n2ZkH9lXw3XfroxbKbhTHHVc77J0HT8Z0FXsVeV0rss7OrIK47z5+HiecUPu/SLeEyXzkedpCSOF4\nqutUJKI6bd++WDlCo036yFNsbkj29xtvuJEjrXhC5EPDXQ/V18GnkpRZ5IsXAwcdVLsvz3nrHGub\nv4+l8Dp3dp+nK1y+oeZp0+wxNorc9310/vl0NaMjj/Rbji9KVeT/+U91EdI8qFwNhLhbxZ0tS6fT\nJm8D1O3YTMsyKc+Va0UnNlYm14YbAuPH68uSh969iykHCKNTNBTXCkuHAJd8f+45uhLUk0/6Lacp\nfeRXXAFccom//FlrJLuEGqB3o8n8ecuX+79Zy1wvUrafrdu8jXP69NpQSZ1zzlPm118XM/rX1+AP\nGdn26HrhDVtYufIocp/1t9FG/vJmaboh+r4HYbC/XVnk7EVYvtxdxyNAleN119nJlZbp68FiMkSf\nd+68QUK+eeMN/riCddahC0PrwKtP23b75JN0MfEiKdP6FdVTiBZ5mbjQg6VWaVGDaXyVo+N/ZFew\nUfHNN+q51lWoXB4u8tUpJ7vN5ZwfojKzfPwxf/v8+fXTCLiqq2++ES8zuNtubsqQkT0Plw92V3UU\noo+8jD4toMDOTkLIToSQyYSQRYSQiYSQrThpCCHkCkLIZ4SQhYSQaYSQQ92ISMnbweIjT57/MZvn\n738vL8tlw8k2CpH7QPd4FplFzlPyWUu47Aghl8jqaZVVqh3rZZ6zz7Jt804f5qFGrRTRp1GKj5wQ\n0hnAPQC6ABgJYA0AYwkh2eP2BHAmgJkAzgHQG8CthJDCLxnPUtQJQ3v1VWDECLOybGfa84XoIcJy\n0UXA7bfrHS/D1LXimpAfDKlfWmcNT9eE0MEq4s036XceRe7zuhdVd2X4yPcC0AvA9UmS3AjgLwD6\nA2jJpEsdCO8BeArAt5WP1PlQ1OuSCLZC77oLuPlmdZ7sMY8+ml8G27S2jB7NV/AqsrKxM/uF8NDy\nBXu9J0+u9/XLfOhFjGoVEYJCF7njfLxdt3VUirx/5fuTzHd/NlGSJC8B+BWAQwG8CaAHgCOSRF79\nor15OoTYPFOr0VcjuPxyM3l4lHHDiWTKyiLzg7Odx4MG1e7jhXA1w414+unAXnvpp2+2B5xu3q2t\nZmGzIZCn3saOBbbbTm/aYl+RTKadnVy1QwjZDsAvADwO4EYAVwK4jRCycZIkC7LpR1WCx+mrVguy\nBv5ppxlKJaCIG0mm/EIlSeiiB7LpZHnHiP6/+mrtPtE0si7RqWdZ6B17/Bdf6HUyp30ORYwWDZn0\nvO6+mw6GOvDA2v0XXwz8+td0gA2PsWOBCy4IL3rFZCQwy8sv0yUeZ88GevQwL1PE+PHjMV5zgIWq\nKtO+/T6V73Q4xYyK/3x5kiRLQTVxewB3JEnyACFkPwDHA9gUQN1sEqkinzIFmDatvtCZM+u3ZSv4\npJPoIhIyfEVwiMrgbS/CtTJ3rnkeJ52kLp8duZq3/nj10doKPPtsvnxdMGWKOk1LS/0DS0T2XGfO\nLG+RCd2FqWVMm0bdRH371m4//HBgpZXqO9V59ckqySlT6JQZm25qLkvRPvLXXqs/v379aAir7DjT\ncni0tLSgpaXlf/8vkQy6USnycQBmAziFEDIPwAgA7wN4FsAyAFMBDACQquNTCSFdAOwLYHElrZA8\nF+VPf6oqcpHCZjs7VTSy9ZTOZ1LkOdiWxV6LF18EfvSjYsvnybHiiuJ803QmD5z0mDSv/fdXrzPr\nimxbHzMG+MMf8uV5wgn0Wt15p1563kPblSFQJPPmAdtuC/zgB9VtX30FbLAB8OCD5cqWReojT5Jk\nMYBhAOaBuks+BzAsSZK0EzOppHsIwGgAfQFcBeALAEclSVLqYleuLfIRI+rfFrL5/uQnallck0YD\n6KLrI5cd4+J88swDsvHG+ctP6dTJXV4saR0VpcR9kVrOTz+dL58QFJ4InmzLl9MVq55/vvq57DJx\ntJrO+fmqA6WXKkmS5wAM5Gxvl/l/MYCLTQr3fWHTqAGdcnSsdl5USzbvqVNrt5vEstvWh81E/TK+\n/BJYbbXabaFYVEkCPP448NFHbvLLEuIAmrLL7Ny51q3iogzbPHy7Vhr1mjXMEH2bDqa8FkSjYLrA\nrqou33pLfYyLxqejNLNvQO++axY5ooPpw/Saa+T7jzqKfpc9N75L8j7gbJVkGotfRuCCLqayNd00\ntr5HOx52GP32Zb3pEOLrZJkWFYusEU+YADzzDNCnT+2KPyHM5idaWi4lfUMK8doXiez8dRTYrFl0\npGxZ+NZPLmmY+cjzoDNi0/XFybpWRPMch/gq73KiKNNyUg4+uBrW5kN5lxnR5BMX7SlJ6BiJrDXM\nOx/d8pLEvM5Np5jIg02/kW4epmlsCNIi95FnkTfVRx8Be+9du+3uu9XHhfKk9zFowfT4uXOBb7/N\nV6Yuvuqd51rxPQDM1dvWuefWuthY14hJv4/N+b73Ho0W4U1xULaP3IWR42PsSUMr8smT/VqhOvDK\nf+45umgGAFx7rV2+o0ebH2NiHZlSlo88W5YrRaibT57yTNZbdY2LctI3oSRx4yPX5cMPgVdeoQO1\nUsrwkYdiWOnQMK4VXqVuuSXt/GIpY95rH7z4ovkx2ddXXR5/HJg0qfpfx+owLcf0tZyXfuxYOjDH\nNUW6VkKYA0UX1qVlI7ePNtNsxM7OCmVOTATkX4Elr6/217+u/c92DOqUCQB77lmNtPBNnh77++6j\nHWA+eeopdz55dsRt0bhQENk3Cpv2mUeOEAa4mRg10UceQFm2spx8st1xrrjqqurvJMmnhNJGe+qp\n9ft8uFZMcWnNLlpEX+GB2nO55hrg/ffdlBHaXCI6PPJIdZpZ1rXC+9aBvWY2IcQm+1xg4yM3pemW\nenNBW3j9kuFyoE2aF+tmEZVTpI88pZ3D1jp7Np0zAwgraiVJzMcFuOSVV6q/bV0rLtuk6zxllOUj\nb/jOziJpBH9l3guqO+iijM5OF/Uf2vVSYXPOf/kL0K2bH3lMYV0rbcFHXsSAoDbvWvERVugjGsIU\nQugAEtYSsiXPq6vp+Te6a4VFpnCKjlpJ3T1lwconcq2w+Ij1tnHhuCozi0m4YdF9CCwNo8ibmSOO\noHGzNmTrMI9VYRpFkhfd8tLfLl0rRdDo7VvkWjGJjrLxkfPSF6Xcy3DhuCAq8gplyuJy5KKrNxTV\nPtub8oknqttCca346si1efMp8s3w1VfpCFrRpGuyFbZMFbmN/7ls14ru9QtBjwVp45i6XEKoSFtE\nHTouevhdkrec9Hh23hvTPENR5D6vjc+HSJZXXgHuvReYPp2/P2uRs3maTggmkmf06OrgOd1jfFHE\nQzTPQ1FGqQFSZfvAQuk8K7rBmvgDUx54oPa/78gKnp+6jOvlOg46RKNDJBN7jbOK3Mai5h1z0UU0\n3p5dFi50H3mIBOVaueceP52aOjTKBZPh0lLMxsefc07t/9//Xq+sFBcKMRSL3DZfnbyLnoIgCyvf\nv/5Vvy1F1yJ39SAMyUcuai82/QYN3dk5cyZ/rvBDDjHvtfflE2aZMEFvxGReOYqyQD74QCxDimo9\nVFNcWKdFKXJXcoRofbOo2t1KK1V/ZzstZYpc9DbVaOGHIb/FZylFkZ92GjB4sPjmDu0GGDQIuOIK\nt3kuWFD9nUfJhVZXIkJW5FlcWc1lXhsfZdv04aTccQfw9tu122bM4Ke1LSMvvOvqus35OqegXCui\nbb7K4u0Xdby4Xu0lXXQ4lIZShqXBLmorIzTXSlt4yLLb09BU087ObL5PPVX7f/311eWqZAyBvAaJ\ni3MLTpEXmQdPMegql7xkrROA/0paFCHfKCmhxJHr1lWZS73ptB+TB5at8mmUzmLTSLm8eaQ0tI9c\nhk0lhRaTqrMgQjoxkauwPhd5+cTFAzcUi1wXG0We9xx9hDzy6sfm3EJun4BdZI4pTeVaWbSIfufp\nDXeNSyXBs7azsNZlWg/TptVvi1Qpo7MzT5mbbWZ/rC5PPw0sWeIn76xFrqvkRJY8e2/rHB+ijzx2\ndjKkK6O76Oz0EbXy0Uf1i76aXECdtDxFPmSIWrYsJucfaiNUkSR0tRjRwBUf5blgxx3tj9U1aAYP\nBl54ofrfZpZCkYKW1YONj9yUMsIPbQih074URZ6djCcEWFmmTatfL9BEVh1fripN0W8mc+YUW54p\np57qb/Wn7LUdMACYOFGcvogH4hZbyPfPng08/zz97WNxakDe2VmWj9wXZfnI8xzHUooilympt98G\nFi+u3+668mR8/XX9Np5MImwtcpb0rcUUXl6ffUaXxZMxZw5VDr7Ie51UoWqu4cXaFwnrZuMxahSw\n8870dxF+dZM4cp1yXHTGuqYMH3lDd3bKKuiVV4CXXiqmzDff5KfNLp8GAAsX6pclujj33lv9nXZ2\ninDZaTV5Mv00Kq+8IreQ8yKLjgjVHcUuI8cOpXfZbmSdwDYWeWrZq45vNB95CJ6FUi3ytAL+9a/q\n7yJdCoMHV3+rhs268EUffHD1t8q10myxynnIzvPiGtM6fPZZP3LYMnRo/SLkLiK5RAp1/ny9kZ28\n/E0ejLzO0WZo703jI882gt13r77Wu5pVzTXpvBM6uHCt2FzsUK1HwE+ntE9CrkseaZ9O3pV8RHXN\n5nvBBXZzrYSshMse05KXUhW5iS/cN6pyRW4YHqaK3DaPLEX2IxSJb/lDGGSSUvakWSyizs758+1C\nNlnXim65RaLrI88z7kDmxstDqYqcNxxe9KR3GcqkYtky8/wPO6z62zRqJc/FzTa8RlfaPMpU5I1m\nmdsoFR2FZTJEX5af7SLORbTrouLIm2b2Q1kjCMG1YmJ9p/zjH9XfsouTPiRc+cgbhTznU8YgsUb3\nyfp0FegaDFnFnUeRq2Rq65QaR87D101bZAOQNdY//5l+r7FGdVueBpt9e2jGht7srpUDDgA+/bSY\nsngsWkSjYEQhtry8ZVFEOha+jQvDJy7eitucj1wn9KhoXL5Cy/JK5zVXKXJd2LDIZlTiQLnnVcSA\nlgcfdBe3bjMvzdChQO/ewD77iBVsNj/daQFsXBOh+MhFhNhxH9ykWWW5VlxWsM4Cu+zalTZ5iAhV\nmYfY+IvK34cMrl0nY8bQTkze/mxnp6psWdvlPWhknaO83y7o3ZuWm135KouPPpKmm8aWh2ln56GH\n2o+C9IXOxRetXJ5ic3FDeEX1QVtQ5K7Iey6ijn6bSbNk0R22lq+razVrFnDDDcCXX1a36dy3rgcE\nFdbZSQjZiRAymRCyiBAykRCylSBdH0LIA4SQ+YSQrwkhfxXlKTtxU4v8/feBd95RpwvFR57ia27t\n++/3k2+ZNJOiFeE6Kss0v1RRs1Mwu6h3m87OIq738uVAB2bpeRcPDd9uGRlSdUII6QzgHgBdAIwE\nsAaAsYSQdpl0BMB9AAYD+B2AcwEIZ+7gnczHH4v3qfA1jactOq+WPgYEAX7XFs1DntGQbSn8sEiX\nGntM2h47darfn81bJOM339QbYnnC93y5VlIZ27WTh1g2Eh0U+/cC0AvAuUmS3EgIWQvARQBaALDL\nJ+8KYGsAowH8LkkS6RRTvIuZ9to3w3zksrzS82NvGFeE3Ah5E5Hp8uST7uTI8tRTVSPCNXkHithg\nuwiHag4UHR95durnLDoW+eTJwD33yMvJS2srVeKuXCk2bz95juehesHvX/n+JPPdP5MunUL/EAAL\nCCHfEkJ+YiJIGvokC01MF6TwgctG88tfivelinzllen3lVfyJ+Qqc5mw0FD1J+RhyBDgwgv95e+a\nqVOBgQPd94fwrFHRf5M4clPZRo0CbrlFnN7Ffbp8OZ20zuZtIdQBYqaeWtFppPblEgAHAngfwJWE\nkA11Mz70UPotUmDvvQesuKJubo3DI4/orSikQ8gWeciwsweWhe61++ADYMoUdTqdOcp54YW8uVFM\nB/KwmPrIWTeprwd4qshZyvKRu3owqFwr6SzQfSrfvdPtFf/58iRJljLpHkmS5CFCyI4ABgDoByAz\nLxswf/4o5l9L5UN5/XW+IHlvtlCUXAiDByK1+PKRlzmgZcwYs+N5rhVZHLlpvqp89tsP+O1va7ex\nb+Au65JV5C585L6u8/jx4zF+/HittCpFPg600/IUQsg8ACNAre1nASwDMBVUYT9WSXcIIWQGgIMB\nfAfgNV6mXbqMwoIF/AJFK9XoWBjt2/tbLcU1qovvclmz+NCQ0yj1M2IEsMMOemlNQ3JlSixrfOi6\nVrLI1ux8+GFg+HB1B6dpuePGVRcl6dABOOYYsWuFh4/5x3Ufii0tLWhpafnf/0suuUSYVupaqXRa\nDgMwD8CVAD4HMCxJkvSSJJV0C0H944sBXFtJf1CSJF/w8pUtK7bSSvztOgp6663F+1QrrhTFBRfQ\nbx8LJTSKQgqNEOotlYHn2li4kLpUbr6ZLrbsA5XCcjkbp4hsAECe63L99bQvYeRIuqbpm29S//vr\nr/NdK0B5/m8X7U9lkSNJkucADORsb5f5/zwvnSvyWtrnn+9GjkYkBEUVqeXOO9Vp1l2XLufWowdw\n7bXuZVAp6rzRGbx8ZcqyY0d3nbjjxtG+hA03pAp8o43oeJN586guScMtZa5O3bBgE3epr3tRqciL\nRnSiNlPLRihRkcuR3cR5rLSHHhLve+sttQwff0yVz0YbVbfpDGe3QeVacdXZmVWgLFlFnueceB21\n3boBZ59NI8Y6dpTLKtuWFx/T2DaMItexyEMNDYqEjc5wcxsmTLA/NqW1tZh2zVOwoo5AWx+5ytpe\nYQX+dhPlPn060J8JjmbTX3FFdUm8Xr2A1zg9eI1q9DSMIteJIS96pfXQcB1b3JYhBHjjDX/x/LqD\nQrLWsEulbupacVkeSzqGZMiQ2u02db/hhsBjj9VuS8+tb1/6Sckqct26deFyck1wk2aJOPpodRrR\nfMo6yDpgG4WoyO0Q1c+AAcB99xVbpm063c78hQuBvfem84+z6EStmCLzMZv6k01kmDdPPwbehY/c\nREZfg5waRpGnHH+8eF8ea+X22+2PjTQ2PLdd+mDnjbr1gehm1p0NdMQIvXJmz6YdgZ9/XrtdJ2rF\nRrHqDAjSDXu0QabIdTpibXTKZ5/JBzP5cJU1jGslYk+sU3NOP51++6o7k9d4lzd+v361ecvkyVq1\nIsW6337ijl1eOKWoHJ3tpm8yptfPhe9/4EDgiy/o9kcfpW8/HToABx5Yf2yb6+wEaDiWjLbe2XnE\nEfztb7xRrBzNgO+5bkR+1mwbznZ22rTxWbOANdeUW9Q6IztT1yVvMI8Opg8lFxY5oO8OsfWRZ0n7\n85YvB/bdFzj8cODxx6n+SudYck1DuVYmTZLvb+uKXESIk0LFa6WHqTuCR7oalewYneHp//qXuHxT\n69mnjzybh+0c6LbWfFpeOsvinXfSTlifb8bBKXLZyXbvLj82KoeIa4pyS5n6yG047TTxPpOBLry0\nu+yizte0Lm3PXXeiLhsfuamPn7eATJtY6i3PSfladSfSdim7f8Fl+XfcUZuvSfihSiE//zy/TNed\nnb593i7qm3VT5XmQmRCc6lOdrOr1MBJxSdkWuQvXig4q14orhVRE+GE2va2P3PY8eYpcdN6udFZw\nijwSiVQxHdlpq3xkIztt/ce6A2dMtv/739UImSShC7PMmiWXQce1YlJvovPi+ciLMi4bKmoFkFdM\ntMgjrgn80JnrAAAgAElEQVTBIjfZLiOruGxdKyb3WR5LnvcgOeEEYP58um3BAuCss4D11gP23198\nrK68tj7y7EIfrPXNW5vXh54KziJ38doWibiibB+5afhhnsmtRD5dXctat5xsHiYPq/nzzcozddXa\nnNt//1v7X+Za0ZHBhoZT5GXfWJGIC3TjyItq7yrftcya18k3Pc42jjyPe8fWIs/rI+e5VqKPPBIp\ngbINh6wycDVIJo9rxaSM7H+TfHTDD23dsa585Nnt0bWCfD7yGH4YcY3vEZ4pplErpvmoSF0rup2d\nJvmymIQUuniIuvaR25bt2+0bnOqL4YeRSBWfSo6FZwR9+GH1t2u3g05etq4V2bwwqjxMDUle+rQu\ni4xaaThFHokUSdlRK6bKQGZVylwJvDRvv81PW1RAgm45snS64Ye25fPSyFwrtr5+FcEpchUx/DBS\nJD4WyZah2zl2//35y+IpaBchh2x+ee7J1K1lo+hM/fsmsrJ5s643WZ+AqC6btrMzulYirjjzzPx5\n6C7YkBddi9xXVBfPtcIqdlf3lk34oWiZRxdRKyqFrzpvXh+KaIi+TxpOkUciulxyid7KUiHB+qUB\nf/eDbhhcXndANl+TzuO0jDwzB5oeZ+ojlyly3kPY1/WMijziFHbh27JppDe0tN3/+c+12+fPB/72\nt+p/l+eUtUaz9167dnyLPM89esAB5vJ99pl8f5k+cpVFnr7ptLmolUhjs88+ZUvQXJx8Ml2DUheX\nCqNdu3zhlzwlmndkp84+lQy8fETpVPXJun14ceQ6eTZlZ2eek4rWfIQlZIv8449r/4va7uWX1/53\nNVZi2DB+vqwcqSLnKaQiww95fPGFvBybwUx5Ozuz6PRvtNnOTl/HRpqPkBX5P/9pd9zqq8v3694D\n776r51pJFVWeOVxskVnty5fru1ZMZHPhI2cfitkHb/SRV5A1jLlz3coSMSc05Rnqw902NtvX+fCu\nG2uRu5bBxP/cvr358SqXiQ4ufOSq8ENXBKfIVcgqt1On4uSIhE9oDxWWpUv95OvSciaEb5HPmiVe\nEYhF15Wgcq307Vu/z2QeFl8+8k8/BcaNq8+LlVGnk9jFg7Hh5iOPhE1oyjM0eVKysdEhWOSyqBWW\nadNq4+t79gTmzBHnK/svQ+ZaYUP5bN/i81rqV1wBPPKION8YRx5pWEJSnDzl1Oi4HBDE6xAUdXbK\nsBltrTuYR4SrmRFZTNtuayswYoQ4H174oW78vilNpcib7aZtRBYtKluCKiE9VFQU0XZl9dGxY/02\nF52dOqhcDiJ3jOi4rDtDd2Rn9r/KNaQq/623+Omjj1xB9JGXT1HTvrZVXCr8qVOBkSOBL78Eunat\nV3pz59Jl1AD7uVaymORz773ifbx2xrpaTDs7bePIeYp6hRWAHj2An/0M2HFHddkuCE6R52mo229P\nv1de2Y0skcamGS1yGzeHqB7++ldq/PToIc7/m2/culZsQv5mzADuvlt8TNZXruu+yNs+RHK3a0c7\ng6dMoXXMlsU7Jg4IErDzzvnziDQ+jaTIdbHxkWeH/bO4Hiyje5zpfb722rRTNYXnu7/xxmreuh2h\nLLqDnmz7KXy6z5pSkUciKWl7Ou64cuVQkW33q6yil06HP/5RnSbtGPZlMcrQyb9nz9o64R3z4IP1\n+/L4yHmY+N918oidnRnWXtutHBE7yraCRUPY+/QpVo68rLYaf3tZho5NZIpuWp1zko2oZH3THTvq\nK3IdpWwbLpkn7NIGpSInhOxECJlMCFlECJlICNlKkrYnIeQLQkgrIeRsG4FsB0rIerEjxVG2Ihfd\nnKGv5+orLM00TzbN6afnk0emRGVKTyVXejzPmm5trY3t9hnLLcpb5lrxpaOkzZsQ0hnAPQC6ABgJ\nYA0AYwkhouOuAtC58ttK5IUL67dtuqnesTZ+sYhbylLkxx9fX75oGtZGQPTgydPxKEvP64y7+mr6\n/dBDZnnK5MljkbPHiqKj9t23dtpfFbIHSh5ft60lb4vKTtkLQC8A1ydJciOAvwDoD6Alm5AQsjeA\nfQH8Lr9Y5kTlHQZlXYfNNqvfxsZGh26RZ7FV5C7HYej6cot4SMoscvZ7zz2pP900/NCVXDrpyogj\n71/5/iTz3Z9NRAjpCuB6AD8H8JEz6QzQGYHWDFx6adkSyAnV8g1VrhTe8HiddCw33cR/o/WNzX2n\nGniTJTttrcgiz47mde0jdz13SlmdnaJizwOwAMC/QN0vALA6IUTQ926Pjv8p9Js2D+efX7YEVYYP\nr98Wat03mkVuE7Z34ok07ttFOSbpRKN58w4IYlm2TK1EWWWcKvrsxFWuEclkOhpVxoMPAgsWyNOo\nJs2aUflO+/x7p9sr/vPlSZIsBbAOgE0AvM0c+3MA8wBwbMhRzO8WcDw1QsqI0YzwCUlpqx7iIcnK\no4jOThmqeWlsXSuifZMnV3/b3LuiN/C0vIkT6ffgwUAHiZaz8ZGr9qseYLrXdvz48Rg/fjwuuQQY\nMkSeVmWnjAMwG8AphJBTAIwA8D6AZ0Et8Fcr6a4FcEjlc11l220AxvKzHcV8Wmr2nHyyXCDZEPBm\nV+ZvvVW2BLWErBz32ot+p22i0SxylWslPT8TlixRp3F9D4lcE2PG5CtTNLIzfSCl0W+PPcafz5wn\nm648phE3KkTltbS0YNSoUQBG4Uc/GiXNQ2qRJ0mymBAyDFQ5XwngDQAnJknSSugZJJV0EwFMBABC\nyMqV7VOSJHlH+2wqdO0q3x8t8mJQWWhpmtBIZcq6oEJX5FOn1j6oVYrc5nzWXhuYPr12m++RnSJc\nhB/K0pnqA1V+vP+mbzB5dJSqjpTzkSdJ8hyAgZzt3KaUJMltoNa4FaoGKqqMOL+KW2wVeVnKPUmo\n9dWhA/81NMSHDsvYscAbb1T/q6xFm/PJKnEWnestwsa40u3sPPJI4M476e9s+KGLeO28BiBPsfPy\nXLAA2H13ukhG797V7arrqLvqWXB2iko5iCp+88314sjLvKFV6y2GROiKj8cee1CfKI/Qz8c0aiU9\nn+zizL5wNdeKqaWanc875auv6o/PG2roykfOy+u77+j3449XpxLQ4de/1ksXnCLPrpwC1FbYSivx\nj2sLrpUilZFOWTxlE6rCVL3prbtuMXKI0O3szLpWunUr5m3U1XU1dX2k/m1CavvH9tgjf/4ufOSm\nbLKJmUGnW1Zwivy11+T7L76Yv103/DBURdMslOlakaFS5N27u5PFBlPlkB2RaQubj48h5LbKknf8\nO0yPW69e9XmwnZ2+fOS88nTzMi2bLV/Zvs2K9A/PImcrSzT5USMsaFCGktttN7vjbDqgQiZ0WU19\nuuyDSffcOndWpxFhU38ufOSiBxZv0iw2na2PXHWeKsUuSmfzoNDZnxKcIuehc1F0n8Jl3tBluH9+\n8Qu742xdK2Wh44sNmSQRh8ll0wG1Ck73XEePVqdxXW861+Xhh8XHr7UW/7jly/kWOe+3rWw6LhbX\n0Sm2KKNWiiZPz3nofnKXPeRFlSkjJGWpipwISVYera10GlbeGykPG0Uu69NQuSR0LVEVPItZNmpR\nZGGzFvmkSdW5dggBZs/Wl4eHizhx130KKgKyqcTwTuaUU9zlVRShP2hYdOpJ5QazYdtt8x0vQiVX\n2dcmSYD991enu/VW+p0qP5P6zvMGVZZCEx3Ptr3f/Y7WnU3Uik6km+iYVL7Zs4Ef/MCsXNc0hCJn\n1xRM2W039YohPEKyzDbaqPq7f3/949g4VBU+b8BmCqcsu10kCQ2h1YVdFFlXdhPXjQt0feQyROeW\njSOfOVOeXheTdpKW37MnXfvU5qGgSt+wFjnvpO66C5g2rX47O+SYfS0s27oSwcq11lq1T3Gdm4yX\njy90rLcDD/Qvhy55R6HaKgCT6yYja02aROG4dK34xrQzUtTZmX0bZMMUTWUR/Tfxs9v20bgyIIJT\n5DzWWYfGX7IQAlx3HXD99fR/I1jkWQsilR2QT+yT4lr2l1/OV5bugh8m6FzHzp2pP9kEX66VgXVj\nnu2YN8/uONeuFdF8LK7f7PIq8myUWvv2fh5ENn0DvO22suk++Bq6s/PYY+n35MnAjjsCt9xC/5f9\nmqxDu3a1cuoo8hSTOkpfwXnI/NFlPFh06dEDmDNHniaV7Zhj6uvaJa7ynT3bzprM29mZ5bbb5OXl\nxTQfmY+cvQ/S9urCR24yICgUXdMQFrmKG24AfvzjsqUwh20Erl7Rs/TpI4+xFw2EueYaP/Ko0L0x\nZKFnLLvvDtxxRzg3nCt8KHLXU1vIOh99WeR5XCuibSJkbbDo9tawitw2fjMU10oWXxY5ID9n0WyT\ne++tHkwUsnLs0sUsve2r79pr2x3HI68SUqHykReBK4scqFXmojlZ8pTFaxMiX7hJ/0aWNtPZySOU\nQHwTOnWq/W9qkete1LffVqfJcsEF9dts6lM3DlqETpkdOwLjxgEDBvD3f/QR8L3vmZVrqwBuvtnu\nuLywisula8UGG0X06af5ymzXrratdeiQL/xQhG38vKvOzoZV5L7xbXncdJN43yqShe9M5Lr11urU\nnjw22gg47DD9/ABgzTVr/5u8IbCIlv1yyYQJdKraPfesbuvWrfqbN42Dqn5tlZyp5S/DxqpzGX7o\n2rWiylMH0RvDiisCW25Z/Z83asV0VKgpeTs7VQSnyPNUYggW+TbbiPcNG1b7n71I7IrvKvbYAzji\nCHmavA8s20mkTM6Dh47cWUU9axZw0kn58u3XT12uKN+jj7Y7lpeXzTF5rL+8rpVRo8T70lV6VDLI\nEKWfPr020sfGR64qTxWVIgoZLUMPBafI85CGpIWg0Fl69qTfstWPVCsjiTj4YP20WasbkIc36dwU\n2TQ776wvDw+ba9erl9ra9DkgSBTp4RP2DYL3EHr2Wfkxpojqp29f8TGrrip/C83DmmvWhqBuvTX9\nzuNasWnvou1tvrMzj3Xx17/my/+EE+h31nLOSzoSUzbTWvYm2377+nx4smdvpBtvpN/77KMnm+v5\nX0LtAA09/NA0L9aS/vvf6/fzZjrME7ViMwfLo4/mnx5Ylj+7b4MN8pVjU75ofxmdnQ0dR54ltTjz\n3FxnnplvSlxe2T/9qfr1O3uT2YbhpS4G08E6tq/1jUIIirx7d+Cbb9yWSwjfKua9ofAWZdF1rXzw\ngZF4/8N0mtgsMvl4USZF+shNFLDJg3DqVGD+fPqbXQlJRnAWuW9UF/rKK9337uvEuNsq8ry49CmG\njK84fRNcunc22YTGx4tWyuGxzjr6abPw/N0pJkrKpSLXLdOmPEBv5GaesEMe8+fTTtzTT6cBCzfc\nQLerpqMOTpH7HmpvMjmVDuuvX/tf91Uw+9/24ZFnAIMLGkWR+yI9fx3LyaUiX2EF4KijqB9a57gn\nnhAvk6jDkiXmkUwmHbGyPHSxad95fOQ+FPrSpbS/7OWXgQsv1DdAglPkvnnwwfrK0Z1NkDc73T33\n6Jctu8DZ+GtdayZJgPvu0yvfNibWhCQB3nrLTV4+sO1UVrHqqn7yzUN2YWaVkSFrA0uX2g1Ec+Va\nUWEz73yezk3VftsHUGtr7RgBXTdv0Io8a+2y2PaGr7cesPHGdsf+/Of127JWjm1DSn1iNuy3X/22\nTTetn7M9BIt8iy3cy2CCKMrC1nJ0qTjY/d9+m7/c7bZTp9eVX+ZaEeHibc2njzx7LO+37jE623Vo\nba09Z928glLkHTvqVeisWcBOO/mTowx3Qd4RkVlWWql2dkXAzO2jC+84WQTBlCnyWPtGw1dbYVfN\nWXFFN+XmldU2RJVF9YAywYUP3iZSx6VLhfcwsnGzBqXI//IXvXS9eskvGLtgA49Q4szZc3AdKVME\nrA/0mWeq20LoWMyiqiO2TfBC5mynmTWRgd3P/uYpctOyVP5qlfV31lnV32nMtqkMAPCf/+gdm5Iq\nNR3llndAkKlPX+Zf141ayZJ1regSlCLfe+/a/zYXZd484LLL5Gn23Ze/3eWrIA/ZxdRR5D4sKlfp\nfQ38CAXRiFVfrhVVJ5wri1wnn5496VQEqUzZ+8cmxlyXNA+eWzNvmTY+cp1j8rpWGl6R9+iR31ru\n0kXdu57OY57iYmjtkCHmx8gsctcNEqi1ql58kX7vsAOdYMrm3Dt2BP7xj9ptobztmOIyosQFLqZE\nFVn4tnmZdii6rLMdd5Tnn7fd2frIfXd26hKUIs8SmlJI5dlhh/p96XBh28abx0euW+b//V/1d3oO\nt95adYuYsuKKegsGNxo+HqJF52WTv+79tu669uX5vKddz37o8w1WRNMo8jKVt48b6ac/1SvPdWen\njHPOqf5eaSXqFinCbZNOgdBMsPXGLka92271y9Hl9b+yHYWuOzt18zvkELpYh26ey5bp5WsjV16X\nk04/guxhxD44VG8Huu6nplHkZaL76ijbn7VWjj9er+x+/dRpyurUNCGvjL4WdE7levhh+VqlrujW\nDdhsM74MIlQ+8pVX1s9LF9N8eHO4yMiG+uo88MeMqf6WhR9mcTH7oQrfbqWoyEsmrfSuXWv9xtlB\nRKw1zFpaLS38/FSYvMHYrmvoSg6dcn/xC+DII/PLI6J/f/lapUB4rpWiOjZdj6ompDaWXZezzqp2\nLvu0yAEz61lXhrxTcZs8vFKCU+RluFaylrioAnX9aTK/sci/aNM7bsJXX9VadL7I1tFmm5kPJtlw\nQ/727ELSWddFaOg+uFQjc22Vskmbsg2NVB2TDRs0HWxjYtDYWOQTJtBjvviifnCfStbsflvd1fSd\nnY1Kdkk3n+he7DKHkNvM0aHD6afXR834gpVp9Gg994yOIu3Tp+qCcRmFoUtWpl696tOYxEtn92X3\n2wzz95WeEGDhQuCAA+jKVj16mPnIZeW1eddK3hVmUnwqUx+uCZ0G7+o1zsfEZD6GK2e59NLa/2ut\npT93vKtrdsYZNKbZZv5rkWVdROijjrVq2y5Eg6V0zk0FOz7hBz+Qy2Hb1mxldDHCs2k7O//5z+ri\nCDvtZD9h/KRJ9VN8uprMyXaosomyC7ljU+T60MVWce2/P13iq0w22aS6rJgM3df8du30Ix9YbOqQ\nt01Hadi4VWzTZ0nDZAkBLr5YnbfraB5VWl2L3GZkpwnBKfI116yujn7bbXYrwgO0tzzbYy6aLMuF\n1Zg3jyIVt8/OTp++fkLkE6nJ2G8/4JprxPu33bZq/dk+lG2Ur8hqdT0giMf119eHhOqcV1k+cp1y\nbZQye0znztRQ6duXGoKyKB2Rj9z1yM611gL22kt+nJb3khCyE4AbAGwEYCqAE5IkeS2TZgcAfwCQ\nrk3zFICTkyT5Qu8UeOXmUwRHHAFcfbVduXn22xzru7PTBpM3CJ1GrHtOohkS89RJt27Upy5iq62A\nuXPdXXu27XbqBCxezE8nssh18s+yzTbAxIn6xwwZUhv7nnYYyo41ldHWMjV9S+V1duq6adljpk+v\nnYlUNfWEi/v0iSeqZc6eza9rlatYqcgJIZ0B3ANgPoCRAC4EMJYQsmGSJOzA8g0BzAZwC4AWAIcD\n+BaAZiS1e3jrXtowfjwND3ShpHRpNHeLiFTmRx7Rn2xp6FA6JDudRiCblw9MLWCTiIYFC6g7RmWR\ni1wruud94onAT35SPwVFmp/MWEh/uwojTdPlvWYyi1zlWjn3XL0y2LeG1VajHxk8o0Ulm6hehw+n\n04qkdOtWncsmzWOrrdTXRcci3wtALwDnJklyIyFkLQAXgSrrp5l0dyVJcjsVgNwFqsgzQyLCII/1\nO3AgVUgvvED/sxdBxrHHAgcdBDz9dP0+01Axn+y6K3Deee7zzU6IZkOZDzITRZeSRuvI/NCic7JR\n5JtuCuyyC1+R8/Kx6Ty/7Tbg97/Xky2PIufJYbp4tMkDRxcXD3mWLbZQv4Gusop6vQIdH3n/yvcn\nme/+bKIkSdho4T0r3xM08vcKb3kr0/hP9uJNmlQ7+nDttekruSqPW26RL4gsu8myIwRZXEWFpPms\nvLLZOpAqXPvIfWGat0696yyAreNa0X0A5O2I9BHNZOsjz5bFeyjwXCJ5feQmZA2w8eOBadPy5cmS\n5tGunRuLvC5/eeFkJwA3A3gFwCh+KnZzS+XD5mEhlUNMy/c9havqVc+UvPWbZy4JgHY6v/KKebll\ntwuWPC6I0aPpeoxArSI3QaXIVYqPlx5wGy7q2rUiY9w4s/Q2Zcg46CA6Ad377+fPK2Xq1PEAxmPy\nZBrnLkPHIp9R+e5T+U5XuJxBCOlMCPlflwIhZBcAjwF4F8AeSZIw65ywjPrfZ/PNW+r2umxMaV6+\nFzvw2WBlloMr5eZDuYu4+Wa7MkJQ5HnaZio/OwulSx959niVq8HEtSLzVcvksbX6eZg+PH28YbD5\nshb5//0fnX5jyRJ3+mvAgBYAo7D11qOw8cajpGl1LPJxoJ2YpxBC5gEYAeB9AM8CWAYaxTKAELJ1\nJS0A3ARgD0LIvCRJHpZlXtTNadMQs5gOG+bBG+Xou3GHhiiaoFs3+XFFuVZ8lcNT2CJFLpLNdFuK\nTue5LGrFBhuL/OCDq7Kkecjy19mmOt4mbpvHCitUp6Nw7VpRoUySJMliAMMAzANwJYDPAQxjIlbS\nSz8QwIoAOgO4DsDfAHCD/0aMkEcw+LiRZJWRd/AFD9HETIceSgc9mZThuj5CVf7ZicOy+FTkrutk\nrbXE+9jzELVLkUXep099Wl46QuwscpUsvHJk8phcs+7dgbFj5fmp8O1akdVhqsjzTsSVzVvHR671\nLEqS5LkkSQYmSdIpSZJtkiR5tbK9XZIkAyu/b638b1/5bpckyXq8/IqwfmRl+lDcWYYO5W9fdVU6\np7OqzKLdCCYNLe/scC7SFo2Ji+Dzz4E//EF8TQmpzgqo41ph+egjcbkqy82Fj/w3v6n9P2iQWZm8\n/J97Tn18iG7F7LmwFrkLTDpjgxvZ6Zq0sl29PpVB3t51UX4uMensDBFT40Km8NZYgz+Ag3cd11tP\nXZ4L1wpAO80vv1ycftky/sAlkStowABgnXXE5RGijlpZtgz44Q/leZjsK7qzk2eR580zm7czi9w1\nZdzcvBvVJnJCRJ7OVNW8HEXUV1oGu66nCS5cE6EofZmv3vV5fv/7dp2dXbvK882SJLR/5uyz+ftX\nXpkqetEIVFbGdOxE9+7i8tK0qjdN9r6x6agUyamTh62BJBrF3KkT8MYbdH0Bl4ajTl424YfeKcpH\nvs029NvUl8iT789/lr/2mlKWUlP5qU0JRTnz6N4d+Oab2m2PP06VGm+xX5Y8Pk+Zy4WXf3YsxLvv\n1s/NzsvXRMauXemkcu+9J5+ojpDq2wbvYZJNm0emNA+f6QEzpSvTBVtvDfz1r7Qz38W00Q1lkfMq\n3kdnnMkFFg3AycrFLsK8xhrqlWdk5LXIzz/fvmwRotGYvnzkZbq/Ujl3352/uHaK7Nxt+15UdXTT\nTcADD1T/b7ABv65M6rp9e6B3b3U6QPx2oHO+IkU+wXCooO65Fela4fnI99mHrtfqguB95EVaaWnn\njEmP99FH6+Vtu/o8y+mn01d5HUUuS5POGOkSWzeLLquvDjz/fPV/GdZ7//52x+WNJx8+HNhzz/rt\nPLbfXr7qlOp43r5Bg+x8zOz+1lZxOlF+ab3prv3ps7PTRd+T7rFBRK34hPcK4vKGHjmSfrMWzPrr\n0yW2spjGibtYvOKaa/gTKrFrfeo0ApNlz3x2dprMd9HSQuec10nrgzxvFnkV+VlniUcj3n9/vhuf\nl2e2fdi+/ZhcI15np+5i5CmmE9UVYZG7mK5WB1aRqyjdIvc9vD2FrYyOHekw6VSWnj3lx6byFhl/\nvd9+9eXzSGUaOhR46SU/snToALz6qjxNo3Z2Flmmro/cdi3SrNuDvSbZPF34nnVcK3371m6zjVDR\nxabDNNR+nFSulVaqnW6YR+mKvCjlKJqlcOZMOtkN4NcPZ4rpEO327euXwnLFaqvRqTR10OkYFlG0\nj9zmOrKTi9mWp1LktiMsZaF+2dG0ttfF1Ee+wQZ0AKBNuTpl8Mo0TWvb2amKNstLmvePfgTcfrs8\nbSlRK0U/Ad99V+wGWXvt6u8yY6FNfPimx2XJ+/Dcfns3kwNl/fqNZJF36+YmRE4kg03erLGSzVNX\nkWe3X3ttbZihiY9c9obhwgVSlmulKEweNKWHH6bCDh7srwzbdT9TNtnEjRwy0nq48MKq2yeLq6G/\neRgzhrpa7r47Xz5Ll9bH3pd5Q+mU/cAD1DrKW0b2xnTR6fbll3QVeBHZV3Pdsk47jX6n15s9rlcv\nvTxsXUVFUURnpw0mfXalu1ZSnnyyeDmy8OTac0/5DeKKtBf///2/+n2uG0se/yghfEvMtLOzQ4f6\nfb/7nZlcLjCpi/33V8dOy/Ju1w54801xxEaqGAkxf0Bn22j2+GyooW2bYo9TLcLNs8hNo1DY8+CF\n++nkocLkmK5dgXfeoW/4992nnugtxcbgWnFFfflKj1oJCdOQKJcMHVodabr33vrLovFmU/RN3vnI\nefzwh/IJoXzQpYs4/PDww/2UyVtsIq0jNoInz5vWzTfTsRCyPEz7I3gKmDeamV3MOW/nrYhU9vvu\ny+f7t3kTWnddunTft98CCxe6ixnnkRoNQSvyjTcOyzf1+util0aKTzdG+/bVkaaPPMKPpMnW19/+\nVg2vLApVR5wLS68ottuOKgMeG29Mv7//fXflqfzS6UM5bzs77jj5wsOnngocc4xd3jJFvu66dIRz\nNi0ri+w6q0ZDpscuW0a/t9kG2HlnsXy69Wj6UFthBWqRuwg/lpFdKlBGaa6VW2+tHx5dJltuaReJ\nUASi8MfDD68P7/JBtlyea0X0ihnSw5qHas1VE1eKCpFlmlXkrhApsuuuA4YNk8siQmWRp7CW6sEH\nVzu2t9iCDmPnlcObu6V3b2qw6MpoE43je9EZwO7hnLYHnXMqLWqlY0ex1RD6za/DsGHAp5+WLYV7\neAb2iaUAAAoWSURBVP7br7+u3oSu3lpGj6ZvST/9qZv8ePnvuqs6nau2eOut4pkCs4rcxkfOI7Vc\nbXjtNf52tj769RPvYwe0bbklXbB5r73oOR55JDB1an3evPpp107u5pL1yaimzNhhB+DRR+mC6iES\nvCIfPryMUu2wvaE22ICO2nSJK6WSdzDIPvvUDj6SzYJnK/Ouu9KPL0WeHdmblXPvvel8IK4eTDqu\nDNeWYZ65sVVTF3z3XfVtZt11gQ8/rN2vuu6bbQbMmFG7jX0j6dCh/kFk0jczZgxdR1NGx4704RIq\n6RtcsK4V1cxyW20FXHVVMbLo8otfVC24ZnhjsCVJaIfdPfe4zzcktt0WeOqpfHnotpPUVcXesGVb\n5FmyHYNdu1Z/77MP/ZbJ3LcvdaukDytC6h8WrCK3ia0/5xz+rJCNSvAWuYqOHYEzzihbilpUHaE+\nEfnII+4oc+bF5ctr/7tyrdick4mPPGXmTHW+m20GTJkiT6PqI5DVyY47ApddZj5fUlGsuirwwQdm\nx6R9djrTmASpyEMntEZSJKY3um5dlVmnzz4rngOkCLmy0Q+uHtiDBwO77GI+ZawMXn1kH0S2qKbV\nldXLCy+4kcEX995LwxZN6N5dvy1ERa4ghNGUKS6UymOPVdeLZMl2XLHkWc+zER56u+ziJp8DDqi1\nTo85Rm+q15VXrq83F22sc2c6EtVGkesO4QeqvnjT+YFYPvtMPfBOp05cjJL1gez+ckFU5AI++0y+\nEnqRuHSt7LEHf/t++wGLFqmPj+4dMTffXPv/hz+Uz/YnI9R65ilIF2GTa66pThNqnYRAHNkpQNaw\nQnvau0I0wKGIuSjiTVqlzPa1+urAoYfWLy8ns3RvvdW7WADCejsOjajIJQwfDhx7bNlS0BsrFIpW\nMjNm6M9n4YNGf2ibyt+lC/D3v4utbF5+PXvSeUF8uw+i0hYTXSsS7rqLv73om/u44+jKKo2gVGw7\nO0X0719uRElZhKq0RNfzk0/4k2O5LCfUOgmB0hV5IyinLLIBMD4JoSGnM7KJaMTOzpAYODDckYYy\nXKwaryKE9h8qpSvyRrw4W2wBzJlTthTFcMwx1flcnnlGPew5SyON4g2BSZPod6j3RZkP5ugjF1O6\nIm9UVGvoNQtsR1ZLi9mxp55aXZhAhcrSL4uyFFdoCiqEsL7Q6iQkSlfk8dW7edG98aZNkz8YBw2i\nPti2xMYbuxloU8aiJB068Mcq5C0nKnIxpSvyeHGaF53BMIB6KT3RnOHNzH//W7YEfHQUeZ7JuiJ2\nlK7II82Lqxu6zLe2ssoOfW78MohGn5jSA7uia6V5iZZZ8xCCj1y2VmyWtqZXSrfIN9qobAnCZd99\ny5YgH1GRNw9DhtCVffbeu5jyoo/cjNIt8ksvBebNK1uKMHnoIfppVKIiL59113WTT/fudGWfIuLF\nRXzve/XzH5U56jckSlfk7dur102MNA7sfC3NoMgb/RX9qKMac8zDtdfWr7C1/vr1yydedx0wcWJx\ncoVK6a6ViD5FLBKbh9dfry6y27UrXeW80RkwAHjiibKlsKddu8Yc8zBihF66vn3rFyA/5RRgp53c\nyxQyJFE4ngghOwG4AcBGAKYCOCFJkrqlWQkhBwK4HEBvAP8GcFySJB9w0iWqMiP1TJ7cmEO3G53W\nVvpmIZoZMhIpCkIIkiThviNKXSuEkM4A7gHQBcBIAGsAGEsIaZdJtyaAuwF8DeAcANsAuC2/6OUy\nfvz4skX4HyolHpKsKhpJ1gkTxjeMEm+keo2yukXlI98LQC8A1ydJciOAvwDoD6Alk+5wAB0B/CZJ\nkusA3A9gZ0JIQy+F2ggXMCXK6ocoqx+irG5RKfJ0netPMt/9FelmCtJFIpFIxDGmUSu6ffgN3tcf\niUQiDUSSJMIPgAMBtAI4t/L/V5X/uwLoDGCFyvaRle3DKv9vr/zvz8kziZ/4iZ/4iR/zj0hXS6NW\nCCGdAHwIYAGAywBcCGARgA0BLAMwNUmSAZXOzg8AvAHayXkpgFeTJBkkzDwSiUQiTpC6VpIkWQxg\nGIB5AK4E8Dmo1Z3OepBU0n0O2uG5CqjCnwjgWD8iRyKRSIRFGUceiUQikbApbIg+IWQnQshkQsgi\nQshEQshWRZVdKf9qQsgsQkgrIeQhZrtQLtt9DmTdkBDyDCHkC0LIt4SQJ9JQzkDl/U9FzvmEkNcI\nIXsGLGtnQsjblXZwTcByflCRMf28FrCsqxBCbieEfE0I+Y4Q8myIshJCjs3UafrpG5qsxsg6O119\nQDtGPwfwHoCTQcMT3wPQrojyKzJcBeoeagXwoEIuYrnPyfkAGATgGQCnVuRuBfA0gE6ByjsGwDEA\nfg5gKYD3A5b1UlBXYSuAqwOW8/1KGzi08tkt4PZ6L2if2WUAjgdwU4j1CqAfU59Hgvb3fQqga2iy\nGp9bIYUAQys3ztmV/5dU/v+o0JMF1kWtIhfKZbvPkZwrZP5/WWkwB4YobyXP1QFsB6okXwixbgEM\nBO24PxtVRR6cnJX8PgBwC4CuOvdRiXW6XiWv2wGsAKB9qLJm5D6kku/o0GXV+RQ1aZbuwCLfZOPb\nRXKtB6CbxT4n55Mkyf/mDSSEfB/AqgDGhiovIWQVALMrf+eDKsodQ5KV0GklbgJwLYBXmF39QpKT\nIQFwNIBjCCFzAJwPGkwQmqybVb63A732ywkhVwGYFaCsLCcBWA7gT6BKPWRZlZQ1jW2oA4ZSuXg9\nwDr73ApDyCYAHgR9zf4Jp5xQ5P0O9NX/DADtQefdsZHHp6zHgb6R3QFgncq2VUCnljCVpYg6/TNo\nxFjqAvijpTy+ZU1nolkJ1GXxAoCfoX5m1RBkpRkSsj6AwQAeS5LkI0t5CtMDOhRlkc+ofPepfPfO\nbC+L9yvfPLm6W+5zAiFkM1C/+ALQV7VZhBBZPZYmb5IkywE8BeApQsgwADsDSG+QUGRdB0BPAJOY\nbUdB3jbLrNNL09+Vt7KzUJ36IiRZ0zyeS5LkfkLIGqBuh1SphSRrykmV7xsyeYcoqx5F+G9Q7fiY\nAeAU0FeQ91AJfyxIhn0AnAfqw3odwAgAA0RyyWT2fT6gDWMWaMfheQCGAzjMViaf8gLYA3QytREA\nRlVknhGarAA2BXBQ5XNxpR08AuoCCkbOiqwDATwE2tl9BoA5oH0Pa4Uma0XeSZX2eiLoFNZLAGwe\nqKwdQd2A7+vopzJlNTqvwgqiVtpkAItBBwxtXeiJ0giAVlC/WPp9tEwu230OZG3JyNoKYHkemXzJ\nC+D7AKaAvjl8BWAcgM1DlJXJf1ClTq8OUU4Aa4I+ZOaA+p1fBrBbiLJW8t4MwIsAFgJ4C8DwgGUd\nXrmvLshsD05Wk08cEBSJRCINTulrdkYikUgkH1GRRyKRSIMTFXkkEok0OFGRRyKRSIMTFXkkEok0\nOFGRRyKRSIMTFXkkEok0OFGRRyKRSIPz/wFLrBjOSnvTHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfaf1be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C150_2.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPY_3YR_RT_SUPP</th>\n",
       "      <th>C150_4_POOLED_SUPP</th>\n",
       "      <th>C200_L4_POOLED_SUPP</th>\n",
       "      <th>gt_25k_p6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7799</th>\n",
       "      <td>PrivacySuppressed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3892</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7800</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7801</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7463</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7802</th>\n",
       "      <td>0.8028375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7803</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RPY_3YR_RT_SUPP  C150_4_POOLED_SUPP C200_L4_POOLED_SUPP gt_25k_p6\n",
       "7799  PrivacySuppressed                 NaN              0.3892       NaN\n",
       "7800                NaN                 NaN                 NaN       NaN\n",
       "7801                NaN                 NaN              0.7463       NaN\n",
       "7802          0.8028375                 NaN                 NaN       NaN\n",
       "7803                NaN                 NaN                 NaN       NaN"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.loc[:,['RPY_3YR_RT_SUPP','C150_4_POOLED_SUPP','C200_L4_POOLED_SUPP','gt_25k_p6']]\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPY_3YR_RT_SUPP</th>\n",
       "      <th>C150_4_POOLED_SUPP</th>\n",
       "      <th>C200_L4_POOLED_SUPP</th>\n",
       "      <th>gt_25k_p6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6281.000000</td>\n",
       "      <td>2372.000000</td>\n",
       "      <td>3663.000000</td>\n",
       "      <td>5935.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.598269</td>\n",
       "      <td>0.477151</td>\n",
       "      <td>0.587888</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.205394</td>\n",
       "      <td>0.202158</td>\n",
       "      <td>0.238559</td>\n",
       "      <td>0.179575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.050505</td>\n",
       "      <td>0.022435</td>\n",
       "      <td>0.027792</td>\n",
       "      <td>0.039216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.438847</td>\n",
       "      <td>0.333250</td>\n",
       "      <td>0.386187</td>\n",
       "      <td>0.378948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.467876</td>\n",
       "      <td>0.630884</td>\n",
       "      <td>0.504378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.774272</td>\n",
       "      <td>0.612556</td>\n",
       "      <td>0.773137</td>\n",
       "      <td>0.634577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RPY_3YR_RT_SUPP  C150_4_POOLED_SUPP  C200_L4_POOLED_SUPP    gt_25k_p6\n",
       "count      6281.000000         2372.000000          3663.000000  5935.000000\n",
       "mean          0.598269            0.477151             0.587888     0.504846\n",
       "std           0.205394            0.202158             0.238559     0.179575\n",
       "min           0.050505            0.022435             0.027792     0.039216\n",
       "25%           0.438847            0.333250             0.386187     0.378948\n",
       "50%           0.588235            0.467876             0.630884     0.504378\n",
       "75%           0.774272            0.612556             0.773137     0.634577\n",
       "max           1.000000            1.000000             1.000000     1.000000"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data = data.replace('PrivacySuppressed', np.nan)\n",
    "final_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPY_3YR_RT_SUPP</th>\n",
       "      <th>C150_4_POOLED_SUPP</th>\n",
       "      <th>C200_L4_POOLED_SUPP</th>\n",
       "      <th>gt_25k_p6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.438402</td>\n",
       "      <td>0.666009</td>\n",
       "      <td>0.289189</td>\n",
       "      <td>0.446136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.767649</td>\n",
       "      <td>0.285309</td>\n",
       "      <td>0.289189</td>\n",
       "      <td>0.652220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.628856</td>\n",
       "      <td>0.285309</td>\n",
       "      <td>0.289189</td>\n",
       "      <td>0.554537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.882455</td>\n",
       "      <td>0.680930</td>\n",
       "      <td>0.239854</td>\n",
       "      <td>0.709142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.849850</td>\n",
       "      <td>0.633327</td>\n",
       "      <td>0.239854</td>\n",
       "      <td>0.569307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.528708</td>\n",
       "      <td>0.633327</td>\n",
       "      <td>0.239854</td>\n",
       "      <td>0.428771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.254125</td>\n",
       "      <td>0.134844</td>\n",
       "      <td>0.239854</td>\n",
       "      <td>0.287206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.349281</td>\n",
       "      <td>0.109873</td>\n",
       "      <td>0.338284</td>\n",
       "      <td>0.606406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.554302</td>\n",
       "      <td>0.109873</td>\n",
       "      <td>0.338284</td>\n",
       "      <td>0.489614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.590853</td>\n",
       "      <td>0.270732</td>\n",
       "      <td>0.164098</td>\n",
       "      <td>0.433466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.563029</td>\n",
       "      <td>0.270732</td>\n",
       "      <td>0.164098</td>\n",
       "      <td>0.612365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.598269</td>\n",
       "      <td>0.270732</td>\n",
       "      <td>0.210753</td>\n",
       "      <td>0.386324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.598269</td>\n",
       "      <td>0.270732</td>\n",
       "      <td>0.945962</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.598269</td>\n",
       "      <td>0.270732</td>\n",
       "      <td>0.260445</td>\n",
       "      <td>0.405150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.606629</td>\n",
       "      <td>0.323541</td>\n",
       "      <td>0.310234</td>\n",
       "      <td>0.512655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.598269</td>\n",
       "      <td>0.323541</td>\n",
       "      <td>0.280347</td>\n",
       "      <td>0.363136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.474266</td>\n",
       "      <td>0.323541</td>\n",
       "      <td>0.280347</td>\n",
       "      <td>0.596552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.781431</td>\n",
       "      <td>0.447984</td>\n",
       "      <td>0.280347</td>\n",
       "      <td>0.616858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.511628</td>\n",
       "      <td>0.447984</td>\n",
       "      <td>0.244617</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.598269</td>\n",
       "      <td>0.297700</td>\n",
       "      <td>0.244617</td>\n",
       "      <td>0.364865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.627608</td>\n",
       "      <td>0.297700</td>\n",
       "      <td>0.244617</td>\n",
       "      <td>0.546617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.598269</td>\n",
       "      <td>0.297700</td>\n",
       "      <td>0.356119</td>\n",
       "      <td>0.417303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.622807</td>\n",
       "      <td>0.297700</td>\n",
       "      <td>0.164837</td>\n",
       "      <td>0.493239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.543044</td>\n",
       "      <td>0.390415</td>\n",
       "      <td>0.156614</td>\n",
       "      <td>0.464606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.718121</td>\n",
       "      <td>0.390415</td>\n",
       "      <td>0.156614</td>\n",
       "      <td>0.373832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.598269</td>\n",
       "      <td>0.390415</td>\n",
       "      <td>0.187935</td>\n",
       "      <td>0.363100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.543230</td>\n",
       "      <td>0.330418</td>\n",
       "      <td>0.187935</td>\n",
       "      <td>0.566547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.598269</td>\n",
       "      <td>0.330418</td>\n",
       "      <td>0.261865</td>\n",
       "      <td>0.407407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.606218</td>\n",
       "      <td>0.189705</td>\n",
       "      <td>0.283015</td>\n",
       "      <td>0.531532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.216789</td>\n",
       "      <td>0.189705</td>\n",
       "      <td>0.283015</td>\n",
       "      <td>0.362565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7756</th>\n",
       "      <td>0.483530</td>\n",
       "      <td>0.215500</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7757</th>\n",
       "      <td>0.289359</td>\n",
       "      <td>0.215500</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7758</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.215500</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7759</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.267500</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7760</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.336700</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7761</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.365400</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7762</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.379900</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7763</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.296800</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7764</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.293900</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7765</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.293900</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7766</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.293900</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7767</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.293900</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7768</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.287700</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7769</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.287700</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7770</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.287700</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7771</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.287700</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7772</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.397900</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7773</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.397900</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7774</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.393300</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7775</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.393300</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7776</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.237000</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7777</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.237000</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7778</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.237000</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7779</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.291900</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7780</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.291900</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7781</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.224700</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7782</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.290300</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7783</th>\n",
       "      <td>0.544562</td>\n",
       "      <td>0.290300</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7784</th>\n",
       "      <td>0.855505</td>\n",
       "      <td>0.259700</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7785</th>\n",
       "      <td>0.424949</td>\n",
       "      <td>0.149700</td>\n",
       "      <td>0.389200</td>\n",
       "      <td>0.504846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7780 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RPY_3YR_RT_SUPP  C150_4_POOLED_SUPP  C200_L4_POOLED_SUPP  gt_25k_p6\n",
       "6            0.438402            0.666009             0.289189   0.446136\n",
       "7            0.767649            0.285309             0.289189   0.652220\n",
       "8            0.628856            0.285309             0.289189   0.554537\n",
       "9            0.882455            0.680930             0.239854   0.709142\n",
       "10           0.849850            0.633327             0.239854   0.569307\n",
       "11           0.528708            0.633327             0.239854   0.428771\n",
       "12           0.254125            0.134844             0.239854   0.287206\n",
       "13           0.349281            0.109873             0.338284   0.606406\n",
       "14           0.554302            0.109873             0.338284   0.489614\n",
       "15           0.590853            0.270732             0.164098   0.433466\n",
       "16           0.563029            0.270732             0.164098   0.612365\n",
       "17           0.598269            0.270732             0.210753   0.386324\n",
       "18           0.598269            0.270732             0.945962   0.504846\n",
       "19           0.598269            0.270732             0.260445   0.405150\n",
       "20           0.606629            0.323541             0.310234   0.512655\n",
       "21           0.598269            0.323541             0.280347   0.363136\n",
       "22           0.474266            0.323541             0.280347   0.596552\n",
       "23           0.781431            0.447984             0.280347   0.616858\n",
       "24           0.511628            0.447984             0.244617   0.504846\n",
       "25           0.598269            0.297700             0.244617   0.364865\n",
       "26           0.627608            0.297700             0.244617   0.546617\n",
       "27           0.598269            0.297700             0.356119   0.417303\n",
       "28           0.622807            0.297700             0.164837   0.493239\n",
       "29           0.543044            0.390415             0.156614   0.464606\n",
       "30           0.718121            0.390415             0.156614   0.373832\n",
       "31           0.598269            0.390415             0.187935   0.363100\n",
       "32           0.543230            0.330418             0.187935   0.566547\n",
       "33           0.598269            0.330418             0.261865   0.407407\n",
       "34           0.606218            0.189705             0.283015   0.531532\n",
       "35           0.216789            0.189705             0.283015   0.362565\n",
       "...               ...                 ...                  ...        ...\n",
       "7756         0.483530            0.215500             0.389200   0.504846\n",
       "7757         0.289359            0.215500             0.389200   0.504846\n",
       "7758         0.544562            0.215500             0.389200   0.504846\n",
       "7759         0.544562            0.267500             0.389200   0.504846\n",
       "7760         0.544562            0.336700             0.389200   0.504846\n",
       "7761         0.544562            0.365400             0.389200   0.504846\n",
       "7762         0.544562            0.379900             0.389200   0.504846\n",
       "7763         0.544562            0.296800             0.389200   0.504846\n",
       "7764         0.544562            0.293900             0.389200   0.504846\n",
       "7765         0.544562            0.293900             0.389200   0.504846\n",
       "7766         0.544562            0.293900             0.389200   0.504846\n",
       "7767         0.544562            0.293900             0.389200   0.504846\n",
       "7768         0.544562            0.287700             0.389200   0.504846\n",
       "7769         0.544562            0.287700             0.389200   0.504846\n",
       "7770         0.544562            0.287700             0.389200   0.504846\n",
       "7771         0.544562            0.287700             0.389200   0.504846\n",
       "7772         0.544562            0.397900             0.389200   0.504846\n",
       "7773         0.544562            0.397900             0.389200   0.504846\n",
       "7774         0.544562            0.393300             0.389200   0.504846\n",
       "7775         0.544562            0.393300             0.389200   0.504846\n",
       "7776         0.544562            0.237000             0.389200   0.504846\n",
       "7777         0.544562            0.237000             0.389200   0.504846\n",
       "7778         0.544562            0.237000             0.389200   0.504846\n",
       "7779         0.544562            0.291900             0.389200   0.504846\n",
       "7780         0.544562            0.291900             0.389200   0.504846\n",
       "7781         0.544562            0.224700             0.389200   0.504846\n",
       "7782         0.544562            0.290300             0.389200   0.504846\n",
       "7783         0.544562            0.290300             0.389200   0.504846\n",
       "7784         0.855505            0.259700             0.389200   0.504846\n",
       "7785         0.424949            0.149700             0.389200   0.504846\n",
       "\n",
       "[7780 rows x 4 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.RPY_3YR_RT_SUPP.fillna(value=final_data.RPY_3YR_RT_SUPP.mean(), inplace=True)\n",
    "final_data.C200_L4_POOLED_SUPP.interpolate(method='nearest', inplace=True)\n",
    "final_data.C150_4_POOLED_SUPP.interpolate(method='nearest',order=3, inplace=True)\n",
    "final_data.gt_25k_p6.fillna(final_data.gt_25k_p6.mean(), inplace=True)\n",
    "last_data = final_data.dropna()\n",
    "last_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.43840178,  0.66600935,  0.28918936,  0.44613582],\n",
       "       [ 0.76764907,  0.28530923,  0.28918936,  0.65221989],\n",
       "       [ 0.62885619,  0.28530923,  0.28918936,  0.55453712],\n",
       "       ..., \n",
       "       [ 0.54456228,  0.2903    ,  0.3892    ,  0.50484577],\n",
       "       [ 0.85550459,  0.2597    ,  0.3892    ,  0.50484577],\n",
       "       [ 0.42494929,  0.1497    ,  0.3892    ,  0.50484577]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = last_data.get_values()\n",
    "array = np.array(X)\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.03394594  0.00983546 -0.00209125  0.0149997 ]\n",
      " [ 0.00983546  0.04532575 -0.00211663  0.00453684]\n",
      " [-0.00209125 -0.00211663  0.05753632 -0.00333943]\n",
      " [ 0.0149997   0.00453684 -0.00333943  0.02458007]]\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=4)\n",
    "pca.fit(array)\n",
    "print(pca.get_covariance())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.37706307  0.32608458  0.2142525   0.08259985]\n"
     ]
    }
   ],
   "source": [
    " print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_components': 4, 'copy': True, 'whiten': False}\n"
     ]
    }
   ],
   "source": [
    "print(pca.get_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 42.24868906  -6.71575706  -0.13698337 -24.56083417]\n",
      " [ -6.71575706  23.56524573   0.61306543  -0.16802532]\n",
      " [ -0.13698337   0.61306543  17.53445057   2.3526571 ]\n",
      " [-24.56083417  -0.16802532   2.3526571   56.02198518]]\n"
     ]
    }
   ],
   "source": [
    "print(pca.get_precision())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
